Utilizing voice biometrics

ABSTRACT

Methods, systems, computer-readable media, and apparatuses for handling calls based on a voice biometric confidence score are presented. In some embodiments, a computing device may receive a voice sample associated with a telephone call. Subsequently, the computing device may determine a voice biometric confidence score based on the voice sample. The computing device then may determine to route the telephone call to a certain endpoint based on the voice biometric confidence score.

BACKGROUND

Aspects of the disclosure relate to computer hardware and software. Inparticular, one or more aspects of the disclosure generally relate tocomputer hardware and software for utilizing voice biometrics.

Large organizations, such as financial institutions, interact with andserve an ever-growing number of customers, who are often located allover the world. As such an organization's customer base continues togrow, it may become increasingly important to efficiently and accuratelyidentify and authenticate customers across many different channels, notonly to provide security and protect customer identity information, butalso to build and improve upon relationships with customers. Someconventional ways of identifying and/or authenticating customers can,among other things, be tedious, inefficient, frustrating, and/orinaccurate, however, and as the customer base grows, the degree to whichthese issues can have an impact likewise increases.

SUMMARY

Aspects of the disclosure relate to various systems, methods,computer-readable media, and apparatuses that provide more convenient,efficient, accurate, and functional ways of identifying, authenticating,protecting, routing, and/or otherwise serving customers utilizing voicebiometrics.

In some embodiments, authentication questions may be selected based on avoice biometric confidence score. For example, a computing device mayreceive a voice sample. Subsequently, the computing device may determinea voice biometric confidence score based on the voice sample. Thecomputing device then may select one or more authentication questionsbased on the voice biometric confidence score.

In other embodiments, one or more calls may be handled based on a voicebiometric confidence score. For example, a computing device may receivea voice sample associated with a telephone call. Subsequently, thecomputing device may determine a voice biometric confidence score basedon the voice sample. The computing device then may determine to routethe telephone call to a certain endpoint based on the voice biometricconfidence score.

In still other embodiments, voice biometrics may be utilized to preventunauthorized access. For example, a computing device may receive a voicesample. Subsequently, the computing device may determine a voicebiometric confidence score based on the voice sample. The computingdevice then may evaluate the voice biometric confidence score incombination with one or more other factors to identify an attempt toaccess an account without authorization.

In yet other embodiments, voice biometrics may be utilized to providerelationship-based service. For example, a computing device may receivea voice sample associated with a customer of an organization.Subsequently, the computing device may determine a voice biometricconfidence score based on the voice sample. The computing device thenmay determine a relationship between the customer and the organizationbased on the voice sample and the voice biometric confidence score.

These features, along with many others, are discussed in greater detailbelow.

BRIEF DESCRIPTION OF THE DRAWINGS

The present disclosure is illustrated by way of example and not limitedin the accompanying figures in which like reference numerals indicatesimilar elements and in which:

FIG. 1A illustrates an example operating environment in which variousaspects of the disclosure may be implemented;

FIG. 1B illustrates another example operating environment in whichvarious aspects of the disclosure may be implemented;

FIG. 2 illustrates an example of a voice biometrics system according toone or more embodiments;

FIG. 3 illustrates a flowchart that depicts an example method ofselecting authentication questions based on a voice biometric confidencescore according to one or more embodiments;

FIG. 4 illustrates an example user interface that may be displayed inproviding one or more authentication questions to a customer servicerepresentative according to one or more embodiments;

FIG. 5 illustrates an example user interface that may be displayed aftera customer has been authenticated according to one or more embodiments;

FIG. 6 illustrates a flowchart that depicts an example method ofhandling calls based on a voice biometric confidence score according toone or more embodiments;

FIG. 7 illustrates an example user interface that may be displayed inrouting a call to a specialized customer service representativeaccording to one or more embodiments;

FIG. 8 illustrates an example user interface that may be displayed aftera call is transferred according to one or more embodiments;

FIG. 9 illustrates a flowchart that depicts an example method ofutilizing voice biometrics to prevent unauthorized access according toone or more embodiments;

FIG. 10 illustrates an example user interface that may be displayedafter an attempt to access an account without authorization has beenidentified;

FIG. 11 illustrates another example user interface that may be displayedafter an attempt to access an account without authorization has beenidentified;

FIG. 12 illustrates a flowchart that depicts an example method ofutilizing voice biometrics to provide relationship-based serviceaccording to one or more embodiments;

FIG. 13 illustrates an example user interface for providing one or morecues to a customer service representative according to one or moreembodiments; and

FIG. 14 illustrates an example data structure that may be used inproviding relationship-based service according to one or moreembodiments.

DETAILED DESCRIPTION

In the following description of various illustrative embodiments,reference is made to the accompanying drawings, which form a parthereof, and in which is shown, by way of illustration, variousembodiments in which aspects of the disclosure may be practiced. It isto be understood that other embodiments may be utilized, and structuraland functional modifications may be made, without departing from thescope of the present disclosure.

As noted above, certain embodiments are discussed herein that relate toutilizing voice biometrics. Before discussing these concepts in greaterdetail, however, an example of a computing device that can be used inimplementing various aspects of the disclosure, as well as an example ofan operating environment in which various embodiments can beimplemented, will first be described with respect to FIGS. 1A and 1B.

FIG. 1A illustrates an example block diagram of a generic computingdevice 101 (e.g., a computer server) in an example computing environment100 that may be used according to one or more illustrative embodimentsof the disclosure. The generic computing device 101 may have a processor103 for controlling overall operation of the server and its associatedcomponents, including random access memory (RAM) 105, read-only memory(ROM) 107, input/output (I/O) module 109, and memory 115.

I/O module 109 may include a microphone, mouse, keypad, touch screen,scanner, optical reader, and/or stylus (or other input device(s))through which a user of generic computing device 101 may provide input,and may also include one or more of a speaker for providing audio outputand a video display device for providing textual, audiovisual, and/orgraphical output. Software may be stored within memory 115 and/or otherstorage to provide instructions to processor 103 for enabling genericcomputing device 101 to perform various functions. For example, memory115 may store software used by the generic computing device 101, such asan operating system 117, application programs 119, and an associateddatabase 121. Alternatively, some or all of the computer executableinstructions for generic computing device 101 may be embodied inhardware or firmware (not shown).

The generic computing device 101 may operate in a networked environmentsupporting connections to one or more remote computers, such asterminals 141 and 151. The terminals 141 and 151 may be personalcomputers or servers that include many or all of the elements describedabove with respect to the generic computing device 101. The networkconnections depicted in FIG. 1A include a local area network (LAN) 125and a wide area network (WAN) 129, but may also include other networks.When used in a LAN networking environment, the generic computing device101 may be connected to the LAN 125 through a network interface oradapter 123. When used in a WAN networking environment, the genericcomputing device 101 may include a modem 127 or other network interfacefor establishing communications over the WAN 129, such as the Internet131. It will be appreciated that the network connections shown areillustrative and other means of establishing a communications linkbetween the computers may be used. The existence of any of variouswell-known protocols such as TCP/IP, Ethernet, FTP, HTTP, HTTPS, and thelike is presumed.

Generic computing device 101 and/or terminals 141 or 151 may also bemobile terminals (e.g., mobile phones, smartphones, PDAs, notebooks, andso on) including various other components, such as a battery, speaker,and antennas (not shown).

The disclosure is operational with numerous other general purpose orspecial purpose computing system environments or configurations.Examples of well-known computing systems, environments, and/orconfigurations that may be suitable for use with the disclosure include,but are not limited to, personal computers, server computers, hand-heldor laptop devices, multiprocessor systems, microprocessor-based systems,set top boxes, programmable consumer electronics, network PCs,minicomputers, mainframe computers, distributed computing environmentsthat include any of the above systems or devices, and the like.

FIG. 1B illustrates another example operating environment in whichvarious aspects of the disclosure may be implemented. As illustrated,system 160 may include one or more workstations 161. Workstations 161may, in some examples, be connected by one or more communications links162 to computer network 163 that may be linked via communications links165 to server 164. In system 160, server 164 may be any suitable server,processor, computer, or data processing device, or combination of thesame. Server 164 may be used to process the instructions received from,and the transactions entered into by, one or more participants.

According to one or more aspects, system 160 may be associated with afinancial institution, such as a bank. Various elements may be locatedwithin the financial institution and/or may be located remotely from thefinancial institution. For instance, one or more workstations 161 may belocated within a branch office of a financial institution. Suchworkstations may be used, for example, by customer servicerepresentatives, other employees, and/or customers of the financialinstitution in conducting financial transactions via network 163.Additionally or alternatively, one or more workstations 161 may belocated at a user location (e.g., a customer's home or office). Suchworkstations also may be used, for example, by customers of thefinancial institution in conducting financial transactions via computernetwork 163 or computer network 170.

Computer network 163 and computer network 170 may be any suitablecomputer networks including the Internet, an intranet, a wide-areanetwork (WAN), a local-area network (LAN), a wireless network, a digitalsubscriber line (DSL) network, a frame relay network, an asynchronoustransfer mode network, a virtual private network (VPN), or anycombination of any of the same. Communications links 162 and 165 may beany communications links suitable for communicating between workstations161 and server 164, such as network links, dial-up links, wirelesslinks, hard-wired links, and/or the like.

Having described an example of a computing device that can be used inimplementing various aspects of the disclosure and an operatingenvironment in which various aspects of the disclosure can beimplemented, several embodiments will now be discussed in greaterdetail.

As introduced above, some aspects of the disclosure generally relate toutilizing voice biometrics. For instance, some aspects of the disclosurerelate to utilizing voice biometrics in providing more convenient,efficient, accurate, and functional ways of identifying, authenticating,protecting, routing, and/or otherwise serving customers. In thediscussion below, various examples illustrating how voice biometrics canbe utilized in accordance with one or more embodiments will bediscussed.

Generally, the term “voice biometrics” may refer to technologies and/ortechniques that can be used to identify, and/or verify the identity of,a person. Such identification and/or verification may be performed byobtaining a sample of the person's voice and comparing the sample to a“voiceprint,” which like a fingerprint, may be a unique or nearly uniqueidentifier that is linked to a particular person. As discussed inseveral examples below, in comparing a voice sample to a voiceprint, acomputing device may obtain a “confidence score,” which may be anumerical value that is indicative of the degree to which the voicesample matches the voiceprint. For example, the closer the match betweenthe voice sample and the voiceprint, the higher the confidence score maybe.

In some instances, the voice sample that is compared to one or morevoiceprints to obtain a confidence score may be obtained in differentways and/or from different sources. For example, such a voice sample maybe obtained from one or more microphones installed at a physicallocation (such as a retail location, e.g., a banking center) during anin-person interaction (e.g., between a customer and a retail associate).Additionally or alternatively, such a voice sample may be obtained overthe phone (e.g., during a conversation between a customer and a customerservice representative, in response to a voice prompt provided by aninteractive voice response (IVR) system, and/or the like). In otherexamples, a voice sample may be obtained over the internet (e.g., via aweb interface) and/or from a software application (e.g., via a mobileapplication being executed on a customer's mobile device).

In addition, a voiceprint to which a voice sample can be compared may,in some instances, be obtained through an enrollment process. In someinstances, an “active enrollment” process may be performed, while inother instances, a “passive enrollment” process may be performed. In anactive enrollment process, a person, such as a customer of a financialinstitution or another organization or entity, may be prompted to speakcertain phrases, and one or more computing device may record and analyzethe sounds associated with the person speaking these phrases. Such anactive enrollment process may, for instance, be performed in person(e.g., at a retail location, such as a banking center) and/ortelephonically (e.g., over the phone with a customer servicerepresentative and/or using an IVR system). Additionally oralternatively, such an active enrollment process may, for instance, beperformed online (e.g., over the internet using a microphone, camera,and/or webcam that may be communicatively coupled to a customer's smartphone, tablet computer, mobile device, and/or other computing device).In a passive enrollment process, instead of prompting a person, such asa customer, to speak certain phrases that can be recorded, a computingdevice may access, analyze, and/or otherwise use previously recordedcalls and/or previously captured recordings of other conversations inwhich the person participated. These previously recorded calls may, forinstance, be obtained from one or more telephonic systems, and thepreviously captured recordings may, for instance, be obtained from oneor more recording and/or monitoring systems (which may, e.g., bedeployed at one or more retail locations, such as one or more bankingcenters). Various techniques may be used to separate out a customer'svoice (or other target person's voice) from a customer servicerepresentative's voice (e.g., in order to create a voiceprint for thecustomer or other target person). In addition, active enrollment andpassive enrollment processes may be carried out on their own or incombination in order to build one or more databases of voiceprints thatcan subsequently be used in identified and/or authenticating customers.In some arrangements, even when passive enrollment processes areutilized, customers and/or other users who may use voice biometricsfeatures may have to actively opt-in to a program to allow voiceprintsto be created and/or have other voice biometrics features enabled. Inother arrangements, customers and/or other users may be automaticallyenrolled in a voice biometrics program and instead may be provided witha choice to opt-out of the voice biometrics program.

By utilizing voice biometrics, particularly in accordance with thevarious embodiments discussed herein, numerous benefits may be providedto a large organization, such as a financial institution, or anotherentity (e.g., other corporate entity, government agency, university, andthe like). For example, several embodiments discussed herein may providefaster, easier, and more efficient ways of securely identifying,authenticating, and/or otherwise verifying the identity of customers. Inaddition, several embodiments discussed herein may provide ways ofreducing customer frustration. For instance, because the vast majorityof callers and customers are legitimately presenting themselves whencalling into an IVR system or visiting a retail location, and becauseseveral of the voice biometrics techniques discussed with respect tovarious embodiments can be used in frictionless and non-intrusive ways,various aspects of the disclosure may enable an organization, such as afinancial institution, to more closely filter out actual attempts atunauthorized access and/or illegitimate usage of services, withoutinterfering with legitimate customers who are using services in theproper and intended ways. Various embodiments that provide these and/orother benefits will now be discussed in greater detail in connectionwith the accompanying figures, beginning with FIG. 2.

FIG. 2 illustrates an example of a voice biometrics system 200 accordingto one or more embodiments. As seen in FIG. 2, system 200 may includeone or more subsystems and/or other elements that each may be configuredto provide different functionalities. In some embodiments, system 200and the various subsystems and/or other elements included therein may beimplemented in a single computing device. In other embodiments, system200 may be implemented in one or more different and/or discretecomputing devices which may, for example, be networked and/or otherwiseconnected to enable the various subsystems and/or other elements toexchange data with each other. For instance, in at least one embodiment,each element illustrated in system 200 may comprise and/or represent aseparate computing device that is configured to provide variousfunctions, such as those discussed below.

In some embodiments, system 200 may include a voice sampling subsystem205. Voice sampling subsystem 205 may, for instance, be configured toreceive one or more voice samples from various sources. For example,voice sampling subsystem 205 may receive voice samples from one or moremicrophones installed at one or more retail locations (which may, e.g.,be stores, banking centers, kiosks, automated teller machine (ATM)alcoves, and/or the like). Additionally or alternatively, voice samplingsubsystem 205 may receive voice samples from one or more telephonesystems (e.g., one or more IVR systems), one or more internet and/orecommerce systems, one or more mobile software applications and/ormobile devices, and/or other sources. In one or more embodiments, thevoice samples received and/or otherwise collected by voice samplingsubsystem 205 may include audio data that is associated with sound clipsand/or other recordings of one or more utterances and/or other speechmade by a person.

In some embodiments, system 200 further may include a voiceprint library210. Voiceprint library 210 may, for instance, be configured to store,maintain, and/or access one or more databases that include voiceprintsfor one or more customers, account holders, other legitimate users,known illegitimate users, and/or other people. Each voiceprint may, forexample, represent and/or include one or more previously recorded and/orpreviously analyzed voice samples that can be used when comparing and/orevaluating voice samples. In some embodiments, instead of or in additionto including previously recorded and/or previously analyzed voicesamples associated with a particular person, a voiceprint may includecharacteristics and/or other data associated with one or more utterancesmade by the person. Such characteristics may, for example, be extractedand/or otherwise determined using various techniques, such as frequencyestimation, hidden Markov models, pattern matching, other techniques,and/or the like.

In some embodiments, system 200 further may include a voice biometricconfidence score determining subsystem 215. Voice biometric confidencescore determining subsystem 215 may, for instance, be configured tocompare one or more voice samples to one or more voiceprints.Additionally or alternatively, voice biometric confidence scoredetermining subsystem 215 may, for instance, be configured to determinevoice biometric confidence scores (e.g., based on the comparisons of thevoice samples to the voiceprints). In one or more embodiments, a voicebiometric confidence score may, for instance, be indicative of thedegree to which a particular voice samples matches a particularvoiceprint. In addition, such a voice biometric confidence score may beused in providing various functionalities in accordance with variousaspects discussed below.

In some embodiments, system 200 further may include an authenticationquestion selection subsystem 220. Authentication question selectionsubsystem 220 may, for instance, be configured to select one or moreauthentication questions to be used in authenticating and/or verifying aparticular person. In one or more embodiments, the selection of suchauthentication questions may be based on a voice biometric confidencescore, as discussed below.

In some embodiments, system 200 further may include a call routingsubsystem 225. Call routing subsystem 225 may, for instance, beconfigured to route incoming and/or in-progress telephone calls tovarious endpoints based on a voice biometric confidence score, asdiscussed below. The endpoints to which calls may be routed by callrouting subsystem 225 may, for example, include various IVR systems,non-specialized customer service representatives, specialized customerservice representatives (who may, e.g., be specialized and/or trained inhandling potentially illegitimate calls), and/or other systems and/orentities.

In some embodiments, system 200 further may include an unauthorizedaccess prevention subsystem 230. Unauthorized access preventionsubsystem 230 may, for instance, be configured to prevent unauthorizedaccess to various systems and/or accounts. For example, unauthorizedaccess prevention subsystem 230 may be used to secure accounts that canbe accessed and/or transacted on in-person, over the phone, over theinternet, via a mobile application, and/or in one or more other ways. Inaddition, unauthorized access prevention subsystem 230 may use one ormore voice biometric confidence scores in combination with one or moreother factors to identify attempts to access accounts withoutauthorization, as discussed below.

In some embodiments, system 200 further may include a relationshipidentification subsystem 235. Relationship identification subsystem 235may, for instance, be configured to determine a relationship between anorganization (e.g., the organization that is using, operating, and/ordeploying voice biometrics system 200) and a customer of theorganization. In some instances, relationship identification subsystem235 may determine such a relationship based on a voice sample (e.g.,obtained from the customer) and/or a voice biometric confidence score(e.g., determined based on the voice sample and/or a voiceprintassociated with the customer). For example, relationship identificationsubsystem 235 may allow for a customer of the organization to beidentified based on their voiceprint, and subsequently approached inview of their relationship to the organization, rather than through thelens of a particular account or product that the customer may be callingin about, visiting a retail location about, and/or otherwise interactingwith the organization about.

FIG. 3 illustrates a flowchart that depicts an example method ofselecting authentication questions based on a voice biometric confidencescore according to one or more embodiments. In some embodiments, themethod illustrated in FIG. 3 and/or one or more steps thereof may beperformed by a computing device, such as computing device 101 or system200. Additionally or alternatively, the method illustrated in FIG. 3and/or one or more steps thereof may be embodied in computer-executableinstructions that are stored in and/or configured to be stored in acomputer-readable medium, such as a memory.

As seen in FIG. 3, the method may begin in step 305, in which a voicesample may be received. For example, in step 305, a computing device(e.g., computing device 101 or system 200) may receive a voice samplefrom one or more sources (e.g., from a telephonic system managing one ormore telephone calls, from a monitoring system collecting audioinformation from one or more microphones, and/or other sources).

In step 310, a voice biometric confidence score may be determined basedon the voice sample. For example, in step 310, the computing device maydetermine a voice biometric confidence score based on the voice samplereceived in step 305. In one or more arrangements, the computing devicemay determine the voice biometric confidence score by comparing thevoice sample with one or more voiceprints (such as voiceprints stored invoiceprint library 210 of FIG. 2) using one or more analysis algorithmsto quantify the degree to which the voice sample matches each of the oneor more voiceprints.

Subsequently, in step 315, one or more authentication questions may beselected based on the voice biometric confidence score. For example, instep 315, the computing device may select one or more authenticationquestions from one or more predefined sets of authentication questionsbased on the voice biometric confidence score determined in step 310.

In one or more arrangements, each authentication question may be aquestion that can be asked to a caller, customer, or other user in orderto determine and/or verify the identity of the caller, customer, oruser. One or more authentication questions may, in some instances, beasked manually by a customer service representative (e.g., in personwith the customer at a retail location, over the phone during a callwith the customer, and/or via other forms of communication). In otherinstances, one or more authentication questions may be askedautomatically by an IVR system, by an ATM machine, and/or by anothercomputing device (e.g., over the phone during a call with the customer,in person while the customer is visiting a retail location, such as abanking center, and/or in other ways). In some instances, one or moreauthentication questions may be asked by a software application beingexecuted on a mobile device. For example, such a software applicationmay prompt a user to provide voice input (and/or other input) inresponse to each of the selected authentication questions, and theprovided input may be checked and/or otherwise evaluated in determiningand/or verifying the identity of the customer.

In one or more arrangements, the computing device may select arelatively larger number of authentication questions (e.g., five, six,seven, and so on) responsive to determining that the voice biometricconfidence score is relatively low (which may, e.g., indicate that thevoice sample does closely not match the voiceprint). Additionally oralternatively, the computing device may select a relatively smallernumber of authentication questions (e.g., one, two, three, or four)responsive to determining that the voice biometric confidence score isrelatively high (which may, e.g., indicate that the voice sample doesclosely match the voiceprint). In some instances, the computing devicemight determine not to select any authentication questions based on thevoice biometric confidence score exceeding a predetermined threshold(which may, e.g., indicate that the voice sample substantially matchesthe voiceprint).

In step 320, the selected authentication questions may optionally beprovided to a customer service representative. For example, in step 320,the computing device may provide the one or more selected authenticationquestions to a customer service representative for use in authenticatingand/or otherwise verifying the identity of the customer, as discussedbelow. As discussed below, in additional and/or alternative embodiments,the computing device may, in step 320, directly provide the one or moreselected authentications questions to the customer (e.g., instead of toa customer service representative). Such questions may, for instance, bedirectly provided to the customer telephonically via an IVR interface,electronically via a web interface and/or software applicationinterface, and/or in one or more other ways.

In some embodiments, a voice sample may be received (e.g., in step 305)via a telephone call. For example, a voice sample may be captured overthe phone during a caller's discussion with a customer servicerepresentative. Additionally or alternatively, a voice sample may becaptured in response to and/or as a result of an IVR system prompting acaller to speak a certain phrase and/or otherwise provide voice input.

In some embodiments, a voice sample may be received (e.g., in step 305)via a microphone installed at a retail location. For example, a voicesample may be captured with one or more microphones installed at aretail location where a customer is physically present. Such a voicesample may, for instance, be captured during the customer's discussionwith an employee or other associate at the retail location (e.g., ateller or greeter at a banking center). In other instances, such a voicesample may be captured during a customer's interaction with a computingdevice. For example, such a voice sample may be captured by an ATMmachine (e.g., the ATM machine may prompt the customer to speak acertain phrase and/or otherwise provide voice input).

In some embodiments, a voice sample may be received (e.g., in step 305)via a mobile application (e.g., a software application that is executingon and/or configured to be executed on a mobile computing device). Forexample, a voice sample of a customer may be captured by a softwareapplication being executed on the customer's mobile device. In someinstances, the software application may be a mobile banking applicationthat may allow a customer to view account balances, deposit checks,transfer funds, and/or otherwise conduct transactions with respect tothe customer's financial accounts.

In some embodiments, determining a voice biometric confidence scorebased on a voice sample (e.g., in step 310) may include comparing thevoice sample to one or more voiceprints. For example, one or morevoiceprints may be stored and/or maintained in one or more centraldatabases, and the voiceprints may correspond to various customers ofthe organization. In some instances, after a voice sample is received,the one or more voiceprints included in the one or more centraldatabases may be searched (e.g., based on the voice sample) and reducedto a subset of the most likely matches during a loose matching process.Such a process may, for instance, include identifying and comparingcertain features of the voice sample to determined and/or previouslyestablished characteristics of the voiceprints (which may, e.g., havebeen previously determined during previous processing of the audiosamples associated with the voiceprints). Once the most likely matchesare determined, a closer matching process may be performed so as todetermine which voiceprint most closely matches the voice sample.Subsequently, the closest matching voiceprint may be further analyzedand compared to the voice sample to determine a voice biometricconfidence score. Additionally or alternatively, where a customerinitially provides input to identify themselves (e.g., a user name, atelephone number, an account number, their first and/or last name,and/or other identifying information), a voiceprint that has beenpreviously established for the customer may be selected and loaded fromthe one or more central databases and used in analyzing the voicesample. Such analysis of the voice sample may include employing variousanalysis techniques, such as frequency estimation, hidden Markov models,pattern matching, and/or other techniques. In addition, the voicebiometric confidence score may reflect the degree to which the voicesample matches the closest voiceprint, as determined based on one ormore of these and/or other analysis techniques.

In some embodiments, selecting one or more authentication questionsbased on the voice biometric confidence score (e.g., in step 315) mayinclude selecting a certain number of questions based on the voicebiometric confidence score. For example, a voice biometric confidencescore that is at or above a first threshold may correspond to a firstnumber of questions, a voice biometric confidence score that is at orabove a second threshold less than the first threshold may correspond toa second number of questions (which may, e.g., be a greater number ofquestions than the first number of questions), and a voice biometricconfidence score that is below the second threshold may correspond to athird number of questions (which may, e.g., be a greater number ofquestions that the second number of questions). Where the voicebiometric confidence score is expressed in terms of a percentage (e.g.,on a scale of 0 to 100, with a score of 100 representing an exactmatch), the first threshold may, for instance, be a score of 75, and thesecond threshold may, for instance, be a score of 45. In some instances,where the voice biometric confidence score is determined to be above acertain threshold (e.g., 95), the computing device may determine not toselect any authentication questions. Rather, in these instances, thecomputing device may verify the customer or caller based solely on thevoice sample (which may, e.g., provide the customer or caller with fullaccess to transact on his or her account(s) as if he or she had beenverified using one or more authentication questions). On the other hand,where the voice biometric confidence score is determined to be below acertain threshold, the computing device may determine to transfer thecustomer or the caller to a specialized customer service representativewho may, e.g., specialize in handling potentially illegitimate calls, asdiscussed in greater detail below.

In some embodiments, selecting one or more authentication questionsbased on the voice biometric confidence score (e.g., in step 315) mayinclude selecting one or more certain types of questions based on thevoice biometric confidence score. For example, depending on the voicebiometric confidence score (determined, e.g., by the computing device instep 310), the computing device may select questions with differentlevels of specificity and/or questions requiring different levels ofknowledge. For instance, a voice biometric confidence score at or abovea first threshold may correspond to a first set of type(s) of questions,a voice biometric confidence score at or above a second threshold lessthan the first threshold may correspond to a second set of type(s) ofquestions, and a voice biometric confidence score below the secondthreshold may correspond to a third set of type(s) of questions. If, forexample, the voice biometric confidence score is relatively high, theone or more types of questions that are selected may be relatively easyto answer, such as the customer's birthdate, the customer's mother'smaiden name, and/or the customer's billing address. If the voicebiometric confidence score is moderately high, the one or more types ofquestions that are selected may be moderately easy to answer, such asthe state in which the customer's account(s) were opened, the retaillocation or banking center that the customer has most recently visited,and/or the expiration date and/or verification value of the customer'scredit card or debit card. If the voice biometric confidence score isrelatively low, the one or more types of questions that are selected maybe more intensive.

For example, if the voice biometric confidence score is relatively low,the authentication questions that are selected (e.g., by the computingdevice in step 315) may be questions that have answers that typicallycannot be found online and/or through public records searches. Examplesof these questions may include the name of a particular store ormerchant that the customer visited and/or shopped with a certain numberof times during a previous billing cycle, the last destination to whichthe customer traveled, the name of a club or group of which the customeris a member, and/or the maximum line of credit on the customer's creditcard account.

In some embodiments, where one or more thresholds are used in selectinga certain number of authentication questions and/or certain types ofauthentications questions, such thresholds may be dynamically adjusted.For example, as a voice biometrics system (e.g., system 200) is used andvarious customers are authenticated and/or verified based on voicesamples and voiceprints, the thresholds that are used in determining thenumber and/or types of authentication questions to be asked may beadjusted. For instance, these thresholds may be adjusted upwards and/ordownwards based on metrics and/or statistics gathered about actualattempts at unauthorized access (e.g., with respect to all accountsmaintained by a financial institution, with respect to certain accountsthat are accessible via the voice biometrics system, and/or with respectto other accounts). For example, if certain accounts are targeted byattempts at unauthorized access more frequently than other accounts,customers and/or callers attempting to access these accounts may bepresented with an increased number of authentication questions (e.g., bysystem 200 in step 315) than might otherwise be required for otheraccounts that have not been targeted as frequently.

As indicated above, after the one or more authentication questions areselected, the selected questions may, in some embodiments, be providedto a customer service representative. For example, the one or moreselected questions may be sent (e.g., by the computing device in step320) to a customer relationship management (CRM) application that may beused by a customer service representative who may be interacting withthe customer (e.g., in person or on the phone). The provided questionsmay, for instance, be configured to cause the CRM application and/or thecustomer service representative to prompt the caller or customer toanswer the authentication questions. In addition to providing theselected questions, the computing device also may provide the answers tothe one or more selected questions. As the customer servicerepresentative prompts the caller or customer to answer the questions,the customer service representative may provide input indicating thecustomer's response and/or whether the customer's response was correct.Such input subsequently may be sent back to the computing device (e.g.,system 200) and/or used by the CRM application to verify the customerand/or caller and enable access to the customer's accounts and/or otherproducts (e.g., if the customer correctly answers a sufficient number ofquestions and can thus be considered verified).

Having discussed several examples of the processing that may beperformed in selecting authentication questions based on a voicebiometric confidence score in some embodiments, several user interfacesthat may be displayed and/or otherwise provided will now be discussed ingreater detail with respect to FIGS. 4 and 5. Any and/or all of theexample user interfaces discussed herein may be displayed by a computingdevice, such as computing device 101 or system 200.

FIG. 4 illustrates an example user interface 400 that may be displayedin providing one or more authentication questions to a customer servicerepresentative according to one or more embodiments. As seen in FIG. 4,user interface 400 may include one or more status indicators, such as acustomer name indicator 410, a call status indicator 415, and anenrollment status indicator 420. Customer name indicator 410 may, forexample, include the name of the customer or caller (e.g., if it hasbeen previously obtained during the call or conversation or if it hasbeen estimated based on voice biometrics). Call status indicator 415may, for example, include information indicating whether the customer orcaller has been authenticated and/or whether the identity of thecustomer or caller has been verified and/or otherwise confirmed based onvoice biometrics. Enrollment status indicator 420 may, for example,include information indicating whether the customer or caller isenrolled in one or more voice biometrics programs.

In addition, user interface 400 may include a region 425 in which one ormore authentication questions (such as the authentication questionsselected, e.g., in step 315 of the example method discussed above) maybe presented. For example, region 425 may include one or more questionboxes, such as question boxes 430 and 435, and each question box mayinclude an authentication question and a corresponding answer.Additionally, each question box may have corresponding answer boxes,such as answer box 440 (which, e.g., corresponds to question box 430)and answer box 445 (which, e.g., corresponds to question box 435). Eachanswer box may, for instance, be checked (or not) by a customer servicerepresentative or other user who may be interacting with user interface400 based on whether the caller or customer correctly answers thequestion presented in the corresponding question box. Region 425 alsomay include a next button 450 that may, for instance, allow a userinteracting with user interface 400 to view one or more additionalauthentication questions as part of an authentication and/or identityverification process.

FIG. 5 illustrates an example user interface 500 that may be displayedafter a customer has been authenticated according to one or moreembodiments. As seen in FIG. 5, user interface 500 may include anupdated call status indicator 505 and a service menu 510. The updatedcall status indicator 505 may, for instance, indicate that the identityof the customer or caller has been verified and/or that the customer orcaller has full access to transact on one or more accounts. The servicemenu 510 may, for instance, enable a customer service representative orother user who may be interacting with user interface 500 to accessinformation about the customer or caller and/or otherwise serve thecustomer or caller. For example, service menu 510 may include one ormore sections and/or links for providing particular functions to thecustomer or caller, such as an account information section 515 (whichmay, e.g., enable the customer service representative to view and/oredit customer account information) and a messages section 520 (whichmay, e.g., enable the customer service representative to view and/oredit one or more messages for the customer). In addition, service menu510 may include a more button 525 that may allow a user interacting withthe user interface 500 to view one or more additional screens (whichmay, e.g., include additional sections and/or links for providing otherfunctions to the customer or caller).

FIG. 6 illustrates a flowchart that depicts an example method ofhandling calls based on a voice biometric confidence score according toone or more embodiments. In some embodiments, the method illustrated inFIG. 6 and/or one or more steps thereof may be performed by a computingdevice, such as computing device 101 or system 200. Additionally oralternatively, the method illustrated in FIG. 6 and/or one or more stepsthereof may be embodied in computer-executable instructions that arestored in and/or configured to be stored in a computer-readable medium,such as a memory.

As seen in FIG. 6, the method may begin in step 605, in which a voicesample associated with a telephone call may be received. For example, instep 605, a computing device (e.g., computing device 101 or system 200)may receive a voice sample via a telephone call (e.g., from a telephonicsystem managing one or more telephone calls, including calls beinghandled by one or more IVR systems and calls being handled by one ormore customer service representatives).

In step 610, a voice biometric confidence score may be determined basedon the voice sample. For example, in step 610, the computing device maydetermine a voice biometric confidence score based on the voice samplereceived in step 605. Such a voice biometric confidence score may bedetermined based on the voice sample similar to how such a voicebiometric confidence score may be determined in step 310 of the examplemethod discussed above with respect to FIG. 3.

Continuing to refer to FIG. 6, in step 615, it may be determined toroute the telephone call to a particular endpoint based on the voicebiometric confidence score. For example, in step 615, the computingdevice may determine, based on the voice biometric confidence score, toroute the telephone call to a certain endpoint. For instance, certaincalls (which may, e.g., have voice biometric confidence scores within acertain range) may be determined to be potentially illegitimate andaccordingly may be routed to specialized customer servicerepresentatives, as discussed in greater detail below.

In some embodiments, receiving a voice sample associated with atelephone call (e.g., in step 605) may include capturing one or moreutterances that are responsive to prompts provided by an interactivevoice response (IVR) system. For example, a caller's spoken responses toprompts provided by an IVR may be captured by the IVR system and/orobtained by the computing device for use as a voice sample indetermining a voice biometric confidence score. In some instances,determining the voice biometric confidence score (e.g., in step 610) maythus include analyzing the one or more captured utterances.

In some embodiments, receiving a voice sample associated with atelephone call (e.g., in step 605) may include capturing one or moreutterances that are responsive to prompts provided by a customer servicerepresentative. For example, a voice sample may, in some instances, beobtained while a caller or customer is speaking with another person,such as a customer service representative during an in-progress call,and the caller's responses to prompts provided by such a person may becaptured by the telephone system and/or obtained by the computing devicefor use as a voice sample in determining a voice biometric confidencescore. In some instances, determining the voice biometric confidencescore (e.g., in step 610) may thus include analyzing such utterances.

In some instances, if and/or responsive to determining that the voicebiometric confidence score falls below a certain threshold (e.g., duringan in-progress call), determining to route the telephone call (e.g., instep 615) may include providing routing information to a customerservice representative, where the routing information is configured tocause the customer service representative to transfer the telephone callto a specialized customer service representative for handling thein-progress call as a potentially illegitimate call. For example, if acaller is speaking with a generic customer service representative and,for instance, answering one or more authentication questions, but thevoice biometric confidence score for the caller is falling and/orotherwise trending downward over the course of the call, the computingdevice may generate and provide such routing information to inform thecustomer service representative that the call should be transferred to aspecialized associate who may have special training in handlingpotentially illegitimate calls. In other words, in analyzing voicebiometrics during an in-progress voice call with a customer servicerepresentative, the computing device (e.g., system 200) may prompt thecustomer service representative to do a warm transfer of the call to aspecialized customer service representative based on determining thatthe voice biometric confidence score is below a certain threshold and/orbased on determining that the voice biometric confidence score hasdropped by a predetermined amount beyond a threshold during the courseof the call. Additionally or alternatively, one or more of thesethresholds may be dynamically adjusted over time based on statisticsand/or call metrics about voice biometric confidence scores forpreviously flagged calls that were later able to be authenticated and/orverified as being the actual customer and/or not an actuallyillegitimate call.

In some embodiments, based on and/or responsive to a telephone callbeing transferred to a specialized customer service representative(where, e.g., the voice biometric confidence score falls below apredetermined threshold as in the examples discussed above), it may bedetermined (e.g., by a computing device, such as system 200) to initiaterecording and/or analysis of the telephone call. For example, if thecomputing device routes the call (or causes the call to be routed) to aspecialized customer service representative for having a relatively lowvoice biometric confidence score, the computing device may beginrecording and/or analyzing the caller's speech. The results of thisanalysis may, for instance, then be added to a database of potentiallyillegitimate callers, along with any additional information about thecall, including historical information, such as the date of the call,the time of the call, the origin of the call, and/or other information.Additionally or alternatively, the computing device (e.g., system 200)may provide the specialized customer service representative with theability to tag the call as illegitimate at any point, and such a tag maycause the recording to be saved and/or sampled to create a defensivevoiceprint for use in identifying the illegitimate caller in the future.In addition, the specialized customer service representative may elicitthe caller to say certain phrases and/or continue speaking in order toobtain an optimal voice sample for creating such a defensive voiceprint.Similarly, the computing device (e.g., system 200) may be configured toprompt the caller (e.g., via an IVR system) to say certain words and/orphrases for creating such a defensive voiceprint.

In some embodiments, the endpoint to which the computing device maydetermine to route the call may be a specialized customer service linethat is configured to handle potentially illegitimate calls. Forexample, the specialized customer service line may be monitored and/oranswered by one or more specialized customer service representativesand/or one or more specialized telephone systems, including one or morespecialized IVR systems (which may, e.g., prompt the caller to answerone or more specially selected authentication questions for use increating a defensive voiceprint). Additionally or alternatively, thespecialized customer service line may be configured such that all callsare recorded and analyzed for future use in identifying potentiallyillegitimate calls.

Having discussed several examples of the processing that may beperformed in handling calls based on a voice biometric confidence scorein some embodiments, several user interfaces that may be displayedand/or otherwise provided will now be discussed in greater detail withrespect to FIGS. 7 and 8. Any and/or all of the example user interfacesdiscussed herein may be displayed by a computing device, such ascomputing device 101 or system 200.

FIG. 7 illustrates an example user interface 700 that may be displayedin routing a call to a specialized customer service representativeaccording to one or more embodiments. In particular, as seen in FIG. 7,user interface 700 may include a notification 705. Notification 705 may,for instance, be configured to alert a customer service representativeas to the potentially illegitimate nature of the call (e.g., that thecaller may be attempting to access one or more accounts withoutauthorization). Additionally or alternatively, notification 705 may beconfigured to cause the customer service representative to transfer thecall to a specialized customer service representative (e.g., byinstructing the customer service representative to transfer the call toa particular line or extension).

FIG. 8 illustrates an example user interface 800 that may be displayedafter a call is transferred according to one or more embodiments. Inparticular, user interface 800 may be displayed to a specializedcustomer service representative as a potentially illegitimate call isbeing transferred in to such a representative. As seen in FIG. 8, userinterface 800 may include a notification 805. Notification 805 may, forexample, be configured to alert the specialized customer servicerepresentative that the caller may be attempting to access one or moreaccounts without authorization. In addition, notification 805 may beconfigured to include additional information about the nature of thecall (e.g., indicating, in the illustrated example, that the caller hasrequested to close one or more accounts, yet is calling in from atelephone number that is not registered with the one or more accounts).Additionally, notification 805 may be configured to include informationabout the voice biometric confidence score for the caller (e.g.,indicating, in the illustrated example, the calculated voice biometricconfidence score for the caller and the relative range in which thevoice biometric confidence score falls).

FIG. 9 illustrates a flowchart that depicts an example method ofutilizing voice biometrics to prevent unauthorized access according toone or more embodiments. In some embodiments, the method illustrated inFIG. 9 and/or one or more steps thereof may be performed by a computingdevice, such as computing device 101 and/or system 200. Additionally oralternatively, the method illustrated in FIG. 9 and/or one or more stepsthereof may be embodied in computer-executable instructions that arestored in and/or configured to be stored in a computer-readable medium,such as a memory.

As seen in FIG. 9, the method may begin in step 905, in which a voicesample may be received. For example, in step 905, a computing device(e.g., computing device 101 or system 200) may receive a voice samplefrom one or more sources (e.g., similar to how a voice sample may bereceived in step 305 of the example method discussed above with respectto FIG. 3).

In step 910, a voice biometric confidence score may be determined basedon the voice sample. For example, in step 910, the computing device maydetermine a voice biometric confidence score based on the voice samplereceived in step 905. Such a voice biometric confidence score may bedetermined based on the voice sample similar to how such a voicebiometric confidence score may be determined in step 310 of the examplemethod discussed above with respect to FIG. 3.

Continuing to refer to FIG. 9, in step 915, the voice biometricconfidence score may be evaluated in combination with one or more otherfactors to identify an attempt to access an account withoutauthorization. For example, the computing device may evaluate the voicebiometric confidence score in combination with one or more of the phonetype, phone number authenticity, call origin, phone number history, andcall purpose, as discussed in greater detail below, so as to determinewhether the caller or customer is attempting to access one or moreaccounts without authorization.

Subsequently, in step 920, based on and/or responsive to identifying anactual attempt at unauthorized access, the voice sample may optionallybe analyzed. For example, in step 920, the computing device may analyzethe voice sample and/or create, based on such analysis, a defensivevoiceprint for use in identifying future attempts at unauthorized accessby the caller or customer.

In some embodiments, the voice sample (which may, e.g., be referred toas “the first voice sample” in the discussion below) may be received(e.g., in step 905) via a first channel, and a second voice sample maybe received via a second channel. The second voice sample may bedifferent from the first voice sample, and the second channel may bedifferent from the first channel. For example, the first channel may bea telephonic channel (e.g., the first voice sample may be receivedduring a phone call from a telephonic system, such as an IVR system),and the second channel may be a mobile application channel (e.g., thesecond voice sample may be received as an audio sample from a softwareapplication being executed on a mobile device, and the softwareapplication may, for instance, be a mobile banking application). Afterthe second voice sample is received, a second voice biometric confidencescore may be determined based on the second voice sample (e.g., similarto how such a voice biometric confidence score may be determined in theexamples discussed above). In addition, the second voice biometricconfidence score may be evaluated in combination with the one or moreother factors to identify a second attempt to access a second accountwithout authorization.

In some instances, the first channel in the example above may be a firstproduct channel of a financial institution, and the second channel maybe a second product channel of the financial institution. In theseinstances, the techniques discussed above may be used in recognizingillegitimate usage and/or unauthorized access across multiple channelsand/or entry points of the financial institution. For example,illegitimate usage and/or unauthorized access may be identified and/orprevented across different contact centers for card services, home loansand/or mortgage services, brokerage services, and/or other departmentsof the financial institution.

In some embodiments, the one or more other factors with which the voicebiometric confidence score is evaluated may include the phone type,phone number authenticity, call origin, phone number history, and/orcall purpose. The phone type factor may, for instance, refer to whetherthe phone being used by the caller is a landline, cellular phone,internet phone, or some other type of phone. The phone numberauthenticity factor may, for instance, refer to whether the phone numberbeing used by the caller has been spoofed or not. The call origin factormay, for instance, refer to the city, state, and/or country from whichthe caller is calling. The phone number history factor may, forinstance, refer to whether the phone number being used by the caller hasbeen previously used in attempting to gain access to one or moreaccounts with or without authorization. The call purpose factor may, forinstance, refer to the nature of the caller's one or more requests withrespect to the one or more accounts (e.g., whether the caller isrequesting to close one or more accounts, whether the caller isrequesting to transfer funds to and/or from one or more accounts, and/orother types of requests).

As indicated above, in some embodiments, based on a determination thatthe voice sample is associated with an actual attempt to access anaccount without authorization, the voice sample may be analyzed and theanalysis results may be stored in a database of suspicious voiceprints.In some arrangements, the database of suspicious voiceprints may beshared across and/or between various different organization and/orentities. For example, different financial institutions may contributeto and/or use data from such a database. Additionally or alternatively,other types of organizations may contribute to and/or use data from sucha database.

In addition, in some embodiments, based on a determination that thevoice sample is associated with an actual attempt to access an accountwithout authorization, the account that the caller is attempting toaccess may be locked (e.g., so as to prevent any further transactionsfrom being performed with respect to the account), and one or morelegitimate users may be required to call in to unlock the account (e.g.,so as to resume the ability to transact on the account). Additionally oralternatively, where the attempt to access the account withoutauthorization originates from a mobile device and/or a softwareapplication being executed on the mobile device, the mobile deviceand/or the software application may be locked (e.g., until a legitimateuser calls in and/or otherwise authenticates to unlock the mobile deviceand/or the software application).

In some embodiments, evaluating the voice biometric confidence score incombination with one or more other factors may include assigning aweight to the voice biometric confidence score and assigning one or moreadditional weights to the one or more other factors. Thereafter, one ormore of these weights may be dynamically adjusted based on call metrics.For example, the computing device (e.g., system 200) may dynamicallyadjust one or more of the weights assigned to various factors based onstatistics and/or call metrics about voice biometric confidence scoresand/or other data for previously flagged calls and/or voice samples thatwere later able to be authenticated and/or verified as being the actualcustomer and not an actual attempt at unauthorized access.

Having discussed several examples of the processing that may beperformed in utilizing voice biometrics to prevent unauthorized accessin some embodiments, several user interfaces that may be displayedand/or otherwise provided will now be discussed in greater detail withrespect to FIGS. 10 and 11. Any and/or all of the example userinterfaces discussed herein may be displayed by a computing device, suchas computing device 101 or system 200.

FIG. 10 illustrates an example user interface 1000 that may be displayedafter an attempt to access an account without authorization has beenidentified. In particular, as seen in FIG. 10, after an attempt toaccess an account without authorization has been identified, anotification 1005 may be displayed in user interface 1000. Notification1005 may, for example, be configured to inform a customer servicerepresentative and/or other user who may be interacting with userinterface 1000 that the caller or customer may be attempting to accessone or more accounts without authorization (e.g., by indicating, as inthe illustrated example, that the caller's voice sample matches avoiceprint that has been associated with previous attempts atunauthorized access via other channels and/or entry points of theorganization).

FIG. 11 illustrates another example user interface 1100 that may bedisplayed after an attempt to access an account without authorizationhas been identified. In one or more arrangements, user interface 1100may be displayed on a mobile device, for instance, after a user of themobile device attempts to access one or more accounts withoutauthorization (e.g., using a software application being executed on themobile device, such as a mobile banking application). As seen in FIG.11, user interface 1100 may include a notification 1105 that may beconfigured to inform the user that collected voice biometrics (e.g., thevoice sample received in step 905 of the example method discussed above)do not match and/or that one or more accounts have been lockedaccordingly. For instance, as in the illustrated example, notification1105 may inform the user of a voiceprint mismatch, indicate that theuser account has been logged out and/or that the account password hasbeen reset, and/or indicate that the user must call in and/or otherwisecontact the organization to verify his or her identity and/or unlock theone or more locked accounts.

FIG. 12 illustrates a flowchart that depicts an example method ofutilizing voice biometrics to provide relationship-based serviceaccording to one or more embodiments. In some embodiments, the methodillustrated in FIG. 12 and/or one or more steps thereof may be performedby a computing device, such as computing device 101 or system 200.Additionally or alternatively, the method illustrated in FIG. 12 and/orone or more steps thereof may be embodied in computer-executableinstructions that are stored in and/or configured to be stored in acomputer-readable medium, such as a memory.

As seen in FIG. 12, the method may begin in step 1205, in which a voicesample associated with a customer of an organization may be received.For example, in step 1205, a computing device (e.g., computing device101 or system 200) may receive a voice sample from one or more sources(e.g., similar to how such a voice sample may be received in step 305 ofthe example method discussed above with respect to FIG. 3), where thevoice sample is of a customer of an organization speaking one or morewords and/or phrases.

In step 1210, a voice biometric confidence score may be determined basedon the voice sample. For example, in step 1210, the computing device maydetermine a voice biometric confidence score based on the voice samplereceived in step 1205. Such a voice biometric confidence score may bedetermined based on the voice sample similar to how such a voicebiometric confidence score may be determined in step 310 of the examplemethod discussed above with respect to FIG. 3.

In step 1215, a relationship between the customer and the organizationmay be determined based on the voice sample and/or based on the voicebiometric confidence score. For example, in step 1215, the computingdevice may determine the relationship between the customer and theorganization, for instance, as illustrated in the examples discussedbelow.

In step 1220, one or more cues may optionally be provided to a customerservice representative. For example, in step 1220, the computing devicemay provide (and/or may cause to be provided) one or more cues to acustomer service representative based on the relationship determined instep 1215, for instance, as illustrated in the examples discussed below.

In step 1225, it may optionally be determined whether to ask thecustomer one or more authentication questions. For example, in step1225, the computing device may determine whether to ask the customer oneor more authentication questions, for instance, as illustrated in theexamples discussed below. Additionally or alternatively, if thecomputing device determines to ask the customer one or moreauthentication questions (e.g., in step 1225), the computing device mayselect one or more authentication questions and/or cause suchauthentication questions to be asked of the customer (e.g., as discussedabove with respect to the example method illustrated in FIG. 3).

In some instances, in determining the relationship between the customerand the organization (e.g., in step 1215), the customer may beidentified based solely on their voice sample (e.g., the voice samplereceived in step 1205) if the voice biometric confidence score is highenough and/or exceeds a predetermined threshold. Additionally oralternatively, if the voice biometric confidence score is below acertain threshold, the customer may be asked to provide additionalidentifying information, such as their name and/or account number,and/or may be asked one or more authentication questions.

By performing one or more steps of the example method illustrated inFIG. 12, the relationship between the customer and the organization maybe determined and/or identified based on the customer's voiceprint.Moreover, such a relationship may be determined, identified, and/orconsidered without regard to the particular products and/or accountsthat the customer is contacting the organization about. Indeed, byutilizing this relationship-based service model, the customer may beapproached based on their relationship to the organization (which may,e.g., be a financial institution), rather than through the lens of aparticular account or product that the customer has, wants, and/or iscontacting the organization about. For example, a customer's voicesample may be used as authentication credentials across various and/orall channels of an organization, such as a financial institution,regardless of which account the customer may be trying to access. Someexamples of these channels may include a credit card account managementIVR system, a checking account management IVR system, a brokerageaccount management IVR system, an ATM, and/or in-person banking.Additionally or alternatively, the customer may be assigned a uniquerelationship identifier that may be used in identifying the customer andidentifying the one or more accounts that are linked to and/or owned bythe customer.

In some embodiments, determining the relationship between the customerand the organization (e.g., in step 1215) may include retrievinginformation associated with at least one of the customer's name,address, accounts, products (which may, e.g., include information aboutthe services and/or goods that the customer uses and/or purchases fromthe organization), local retail location (which may, e.g., includeinformation about the retail location that is nearest to the customer'shome address), physical visit history (which may, e.g., includeinformation about the customer's previous visits to retail locationsoperated by the organization), and online usage history (which may,e.g., include information about the customer's previous usage of one ormore websites and/or applications provided by the organization).

As indicated above, in some embodiments, one or more cues may beprovided to a customer service representative based on the voicebiometric confidence score. Such cues may, for example, includebackground information about the customer (e.g., the customer's nameand/or address) and/or predictive information for the customer, such asinformation about the customer's predicted needs and/or interests, oneor more targeted offers, and/or other information that is specific toand/or selected for the customer. In some instances, the voice biometricconfidence score may additionally or alternatively be used indetermining whether to personally engage with the customer regarding hisor her more detailed physical visit history information and/or onlineusage history information.

In some instances, the voice sample received in step 1205 may bereceived via a telephone call (e.g., as in other examples discussedabove). In other instances, the voice sample received in step 1205 maybe received via a microphone installed at a retail location (e.g., as inother examples discussed above). Where a voice sample is obtained fromsuch a microphone, a customer may, for instance, be identified at abanking center or at an ATM. In still other instances, the voice samplereceived in step 1205 may be received via a mobile application (e.g., asin other examples discussed above). In some arrangements where the voicesample is received via a microphone (e.g., during an in-personinteraction between the customer and a bank teller or other employee,during an interaction between the customer and an ATM machine, and/orthe like), the voice sample may be received in combination with an imageand/or video of the customer that may, for instance, be captured using acamera or other image capture device that is installed at the samelocation (or substantially near) as the microphone. In these instances,the image and/or video of the customer may be used in combination withthe voice sample to identify and/or authenticate the customer. Forexample, if the image of the customer matches an image on record of thecustomer, the biometric confidence score may be increased, whereas ifthe image of the customer does not closely match the image on record ofthe customer, the biometric confidence score may be decreased. In someinstances, where a voice biometric identification is confirmed and/orenhanced by an image biometric analysis, the computing device (e.g.,system 200) may set one or more reliability flags, which may enable thecustomer to have more complete access to transact on one or moreaccounts than might otherwise be granted without such reliability flagsbeing set.

As indicated above, in some embodiments, it may be determined, based onthe voice biometric confidence score, whether to ask the customer one ormore authentication questions. For example, if the voice biometricconfidence score determined in step 1210 is relatively low, thecomputing device may determine to select and/or ask one or moreauthentication questions of the customer (e.g., as in other examplesdiscussed above, such as those discussed with respect to FIG. 3).

In some embodiments, one or more data records used by and/or otherwiseassociated with the organization may be stored and/or maintained at therelationship level (e.g., rather than at the account level). Inaddition, such data records may be shared within and/or accessible toall departments and/or lines of business within the organization. Forexample, the organization may deploy consistent data records at anenterprise level across the entire organization, and customer data maybe stored as and/or retrievable by a name and/or identifier inassociation with a particular voiceprint. Additionally, informationabout the particular products and/or accounts that are used by and/orheld by a particular customer may be stored in one or more sub-fields ofsuch a data structure.

Having discussed several examples of the processing that may beperformed in utilizing voice biometrics to provide relationship-basedservice in some embodiments, an example user interface that may bedisplayed and/or otherwise provided, as well as an example datastructure that may be utilized, will now be discussed in greater detailwith respect to FIGS. 13 and 14.

FIG. 13 illustrates an example user interface 1300 for providing one ormore cues to a customer service representative according to one or moreembodiments. The example user interface 1300 shown in FIG. 13 may, forexample, be displayed by a computing device, such as computing device101 or system 200, in one or more arrangements. As seen in FIG. 13, userinterface 1300 may include a notification 1305. Notification 1305 may,for example, be configured to provide one or more cues to a customerservice representative, for instance, after a caller or customer hasbeen identified using voice biometrics. In the example illustrated inFIG. 13, for instance, notification 1305 may include informationindicating that the caller's voice biometric score is relatively highand/or additional information that is configured to cue the customerservice representative to ask the caller about selected offers, goods,and/or services.

FIG. 14 illustrates an example data structure 1400 that may be used inproviding relationship-based service according to one or moreembodiments. In particular, data structure 1400 may be used by anorganization, such as a financial institution, to store informationabout various customers in association with one or more voiceprints forsuch customers. In addition, such a data structure may be used by theorganization to approach customers at the relationship level, ratherthan at the product level.

As seen in FIG. 14, data structure 1400 may include one or more fieldsand/or sub-fields in which different types of information may be stored.While the example illustrated in FIG. 14 includes particular numbersand/or types of fields, other numbers and/or types of fields maysimilarly be included in a data structure in addition to and/or insteadof those illustrated in FIG. 14 in other embodiments. In the illustratedexample, data structure 1400 may include a relationship identifier field1405, a customer name field 1410, a voiceprint information field 1415,an account types field 1420, a product information field 1425, a localretail locations field 1430, a physical visit history field 1435, and anonline usage history field 1440.

In one or more arrangements, relationship identifier field 1405 mayinclude a unique identifier assigned to the relationship between theparticular customer and the organization. Customer name field 1410 may,for instance, include the first, middle, and/or last name of thecustomer. Additionally or alternatively, customer name field 1410 alsomay include other identifying information for the customer, such as thecustomer's home and/or work addresses, the customer's phone number(s),the customer's email address(es), the customer's username(s), and/or thelike. Voiceprint information field 1415 may, for example, include one ormore voiceprints for the customer that can be used in connection withvarious voice biometrics functionalities, such as those discussed above.Account types field 1420 may, for instance, include information aboutthe various accounts that the customer has with the organization and/orwith other organizations. Product information field 1430 may, forexample, include information about the one or more products that thecustomer has, one or more products that the customer has expressedinterest in, and/or one or more products that the customer may beinterested in (e.g., as determined based on one or more predictivealgorithms). Local retail locations field 1430 may, for example, includeinformation one or more retail locations that are physically near thecustomer, including the customer's home, place of work, and/or currentlocation. Physical visit history field 1435 may, for instance, includeinformation about the customer's previous visits to one or more retaillocations, including the date and/or time of such visits, the particularlocations visited, and/or the like. Online usage history field 1440 may,for example, include information about the customer's previous usage ofcomputing resources provided by the organization, such as the customer'swebsite usage, mobile application usage, and/or the like.

Various aspects described herein may be embodied as a method, anapparatus, or as one or more computer-readable media storingcomputer-executable instructions. Accordingly, those aspects may takethe form of an entirely hardware embodiment, an entirely softwareembodiment, or an embodiment combining software and hardware aspects.Any and/or all of the method steps described herein may be embodied incomputer-executable instructions stored on a computer-readable medium,such as a non-transitory computer readable memory. Additionally oralternatively, any and/or all of the method steps described herein maybe embodied in computer-readable instructions stored in the memory of anapparatus that includes one or more processors, such that the apparatusis caused to perform such method steps when the one or more processorsexecute the computer-readable instructions. In addition, various signalsrepresenting data or events as described herein may be transferredbetween a source and a destination in the form of light and/orelectromagnetic waves traveling through signal-conducting media such asmetal wires, optical fibers, and/or wireless transmission media (e.g.,air and/or space).

Aspects of the disclosure have been described in terms of illustrativeembodiments thereof. Numerous other embodiments, modifications, andvariations within the scope and spirit of the appended claims will occurto persons of ordinary skill in the art from a review of thisdisclosure. For example, one of ordinary skill in the art willappreciate that the steps illustrated in the illustrative figures may beperformed in other than the recited order, and that one or more stepsillustrated may be optional in accordance with aspects of thedisclosure.

1. A computing device, comprising: at least one processor; and memorystoring computer readable instructions that, when executed by the atleast one processor, cause the computing device to: receive a voicesample associated with a telephone call; determine a voice biometricconfidence score based on the voice sample; and determine to route thetelephone call to a first endpoint based on the voice biometricconfidence score.
 2. The computing device of claim 1, wherein receivingthe voice sample associated with the telephone call includes capturingone or more utterances that are responsive to prompts provided by aninteractive voice response (IVR) system.
 3. The computing device ofclaim 2, wherein determining the voice biometric confidence scoreincludes analyzing the one or more captured utterances.
 4. The computingdevice of claim 1, wherein receiving the voice sample associated withthe telephone call includes capturing one or more utterances that areresponsive to prompts provided by a customer service representative. 5.The computing device of claim 4, wherein determining to route thetelephone call includes: based on the voice biometric confidence scorefalling below a threshold, providing routing information to the customerservice representative, the routing information being configured tocause the customer service representative to transfer the telephone callto a specialized customer service representative for handling as apotentially illegitimate call.
 6. The computing device of claim 5,wherein the memory stores additional computer readable instructionsthat, when executed by the at least one processor, further cause thecomputing device to: based on the telephone call being transferred tothe specialized customer service representative, determine to initiaterecording and analysis of the telephone call.
 7. The computing device ofclaim 1, wherein the first endpoint is a specialized customer serviceline that is configured to handle potentially illegitimate calls.
 8. Amethod, comprising: receiving, by a computing device, a voice sampleassociated with a telephone call; determining, by the computing device,a voice biometric confidence score based on the voice sample; anddetermining, by the computing device, to route the telephone call to afirst endpoint based on the voice biometric confidence score.
 9. Themethod of claim 8, wherein receiving the voice sample associated withthe telephone call includes capturing one or more utterances that areresponsive to prompts provided by an interactive voice response (IVR)system.
 10. The method of claim 9, wherein determining the voicebiometric confidence score includes analyzing the one or more capturedutterances.
 11. The method of claim 8, wherein receiving the voicesample associated with the telephone call includes capturing one or moreutterances that are responsive to prompts provided by a customer servicerepresentative.
 12. The method of claim 11, wherein determining to routethe telephone call includes: based on the voice biometric confidencescore falling below a threshold, providing routing information to thecustomer service representative, the routing information beingconfigured to cause the customer service representative to transfer thetelephone call to a specialized customer service representative forhandling as a potentially illegitimate call.
 13. The method of claim 12,further comprising: based on the telephone call being transferred to thespecialized customer service representative, determining, by thecomputing device, to initiate recording and analysis of the telephonecall.
 14. The method of claim 8, wherein the first endpoint is aspecialized customer service line that is configured to handlepotentially illegitimate calls.
 15. One or more non-transitorycomputer-readable media having computer-executable instructions storedthereon that, when executed by a computing device, cause the computingdevice to: receive a voice sample associated with a telephone call;determine a voice biometric confidence score based on the voice sample;and determine to route the telephone call to a first endpoint based onthe voice biometric confidence score.
 16. The one or more non-transitorycomputer-readable media of claim 15, wherein receiving the voice sampleassociated with the telephone call includes capturing one or moreutterances that are responsive to prompts provided by an interactivevoice response (IVR) system.
 17. The one or more non-transitorycomputer-readable media of claim 16, wherein determining the voicebiometric confidence score includes analyzing the one or more capturedutterances.
 18. The one or more non-transitory computer-readable mediaof claim 15, wherein receiving the voice sample associated with thetelephone call includes capturing one or more utterances that areresponsive to prompts provided by a customer service representative. 19.The one or more non-transitory computer-readable media of claim 18,wherein determining to route the telephone call includes: based on thevoice biometric confidence score falling below a threshold, providingrouting information to the customer service representative, the routinginformation being configured to cause the customer servicerepresentative to transfer the telephone call to a specialized customerservice representative for handling as a potentially illegitimate call.20. The one or more non-transitory computer-readable media of claim 19,having additional computer-executable instructions stored thereon that,when executed by the computing device, further cause the computingdevice to: based on the telephone call being transferred to thespecialized customer service representative, determine to initiaterecording and analysis of the telephone call.
 21. The one or morenon-transitory computer-readable media of claim 15, wherein the firstendpoint is a specialized customer service line that is configured tohandle potentially illegitimate calls.